Accurate subsurface exploration requires an optimal network of boreholes. This paper proposes a multi-objective approach to optimize the layout of additional exploratory boreholes. In order to illustrate this approach, geochemical analyses of core samples at the eastern part of the Kahang copper deposit, Central Iran, were used. A measure of the grade uncertainty (kriging standard deviation) and a confidence measure on the ore/waste classification were first calculated by implementing ordinary and indicator kriging. An ore value function was then determined to measure the total value of each block by considering the grades of all the effective variables and their ore membership degree derived from a fuzzy treatment of the grades. Finally, a misclassification cost is defined for each block based on the expected economic effects of ore loss and waste dilution. As a result, an index for the selection of additional boreholes was introduced in order to maximize the kriging standard deviation, the ore misclassification cost, and the ore value and to minimize the confidence measure. Applied to the Kahang copper deposit, this index allowed the prioritization of areas for infill sampling, leading to the recommendation for eight vertical and two directional additional boreholes.

Given that energy costs are a significant component of overall processing costs in mineral plants, reducing these costs through process optimization or technology adoption enhances the technical and financial feasibility of a deposit. Geometallurgical modeling plays a key role in understanding the relationship between material characteristics, mine planning, and processing stages, ultimately contributing to more efficient resource management and cost reduction in mineral processing. This study aims to develop a block model for evaluating comminution energy consumption (CEC) and identifying blocks with the highest energy usage potential during the grinding process in a specified region. Therefore, by applying advanced geostatistical techniques, including joint estimation and simulation based on geometallurgical data from multiple mineral processing stages, we predict CEC across the study area. The dataset encompasses 2.754 drill samples and a block model with 4.680 blocks. In this effort, imulation techniques, such as Plurigaussian and Turning Bands, provided more realistic outcomes than cokriging, considering the unique characteristics of geometallurgical data and the limitations of kriging methods.